Empirical Likelihood for Regression Discontinuity Design
Empirical Likelihood for Regression Discontinuity Design
复制标题
断点回归设计的经验似然
DOI：
10.1016/j.jeconom.2014.04.023
复制
发表时间：
2015
影响因子：
6.3
通讯作者：
and Y.Matsushita
中科院分区：
文献类型：
--
作者：
Otsu,T.;K.Xu;and Y.Matsushita
本文提出了基于经验似然的推理方法，用于从不连续性回归设计中识别因果效应。我们考虑锐回归和模糊回归不连续性设计，并将回归函数视为非参数函数。与传统的 Wald 型方法不同，所提出的推理过程不需要渐近方差估计，并且置信集具有自然形状。这些特征通过模拟和实证例子来说明，该例子评估了班级规模对学生学业成绩的影响。此外，对于急剧回归不连续性设计，我们表明经验似然统计量允许高阶细化，即所谓的 Bartlett 校正。还讨论了带宽选择方法。
This paper proposes empirical likelihood based inference methods for causal effects identified from regression discontinuity designs. We consider both the sharp and fuzzy regression discontinuity designs and treat the regression functions as nonparametric. The proposed inference procedures do not require asymptotic variance estimation and the confidence sets have natural shapes, unlike the conventional Wald-type method. These features are illustrated by simulations and an empirical example which evaluates the effect of class size on pupils’ scholastic achievements. Furthermore, for the sharp regression discontinuity design, we show that the empirical likelihood statistic admits a higher-order refinement, so-called the Bartlett correction. Bandwidth selection methods are also discussed.